2 citations · 3 across the 6 of their papers we have counts for
6 papers
Multimodal Semantic-Aware Contrastive Learning For False Negative Mitigation in 3D Medical Imaging
Sara Ketabi, Matthias W. Wagner, Cynthia Hawkins +3
Multimodal Contrastive Learning (CL) has shown significant performance in aligning representations across various data modalities and improving downstream tasks, especially in heal…
Exploring Radiologists' Expectations of Explainable Machine Learning Models in Medical Image Analysis
Sara Ketabi, Matthias W. Wagner, Birgit Betina Ertl-Wagner +2
In spite of the strong performance of machine learning (ML) models in radiology, they have not been widely accepted by radiologists, limiting clinical integration. A key reason is…
Tumor Location-weighted MRI-Report Contrastive Learning: A Framework for Improving the Explainability of Pediatric Brain Tumor Diagnosis
Sara Ketabi, Matthias W. Wagner, Cynthia Hawkins +3
Despite the promising performance of convolutional neural networks (CNNs) in brain tumor diagnosis from magnetic resonance imaging (MRI), their integration into the clinical workfl…
Improving Pediatric Low-Grade Neuroepithelial Tumors Molecular Subtype Identification Using a Novel AUROC Loss Function for Convolutional Neural Networks
Khashayar Namdar, Matthias W. Wagner, Cynthia Hawkins +3
Pediatric Low-Grade Neuroepithelial Tumors (PLGNT) are the most common pediatric cancer type, accounting for 40% of brain tumors in children, and identifying PLGNT molecular subtyp…
Generating 3D Brain Tumor Regions in MRI using Vector-Quantization Generative Adversarial Networks
Meng Zhou, Matthias W Wagner, Uri Tabori +3
Medical image analysis has significantly benefited from advancements in deep learning, particularly in the application of Generative Adversarial Networks (GANs) for generating real…
Open-radiomics: A Collection of Standardized Datasets and a Technical Protocol for Reproducible Radiomics Machine Learning Pipelines
Khashayar Namdar, Matthias W. Wagner, Birgit B. Ertl-Wagner +1
Background: As an important branch of machine learning pipelines in medical imaging, radiomics faces two major challenges namely reproducibility and accessibility. In this work, we…